Predictive Battery Cell Management via Statistical Trending
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Solution Overview
Problem
Lithium-ion battery cells of the same type and specification exhibit varying performance over time, leading to inconsistent performance and potential early degradation, which can impact product reliability and user experience.
Innovation Solution
A predictive battery cell management system that collects performance data from multiple battery cells, compares it to statistical data, and takes actions based on trending deviations to extend battery life and prevent failures, such as adjusting the state of charge or notifying users to replace cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional battery management systems are used that only monitor end-of-charge voltage, then the system complexity is low, but the reliability of battery performance is poor due to inability to detect early degradation trends
Solution Approach 1:
The system performs preliminary analysis by collecting and storing performance data from multiple charge cycles before degradation becomes apparent. Statistical parameters are pre-calculated from historical data to establish baseline performance, enabling early detection of deviations before they impact battery reliability
Solution Approach 2:
The system implements continuous feedback by comparing real-time performance data against statistical thresholds derived from multiple charge cycles. When performance parameters deviate from expected ranges, the system generates alerts and can adjust charging parameters, creating a closed-loop control system that improves reliability through adaptive management
2Measurement precision
If performance data from multiple charge cycles is collected and analyzed, then the measurement precision of battery degradation detection is improved, but the loss of time for data collection and processing increases
Solution Approach 1:
The system collects performance data at selective intervals rather than continuously monitoring every parameter at all times. By sampling key performance indicators at strategically chosen points during charge cycles and comparing against statistical thresholds, the system achieves sufficient degradation detection precision without the time cost of exhaustive continuous measurement
Solution Approach 2:
Statistical parameters and thresholds are pre-calculated from historical performance data during manufacturing or initial use periods. This preliminary analysis enables rapid real-time comparison without requiring extensive computational processing during operational monitoring, reducing the time loss for data analysis while maintaining detection precision
Data Source
AI summary
Predictive rechargeable battery management is provided, which includes obtaining performance data on a battery cell of multiple rechargeable battery cells within a product, and comparing the performance data of the battery cell to statistical data on battery cell performance of a plurality of battery cells. Further, the managing includes determining, based on the comparing, that performance of the battery cell is trending away from the statistical data of battery cell performance of the plurality of battery cells. Further, the managing includes performing a battery-related action based on the performance of the battery cell trending away from that of the plurality of battery cells.